This project revolves around implementing custom text classification using Azure AI Language services. Leveraging the power of Natural Language Processing (NLP), it involves configuring a model through Language Studio, utilizing Azure Blob Storage, and creating a small command-line application for testing in the Cloud Shell. The technology stack includes Azure AI Language services, Cloud Shell, and Visual Studio Code, along with Azure Blob Storage.
- Azure AI Language Services
- Azure Blob Storage
- Cloud Shell
- Visual Studio Code
- Containers
This multifaceted project covers a spectrum of NLP concepts, including but not limited to custom text classification, feature extraction, containerization, data labeling, single-label classification, model training, model testing, model deployment, and the practical application of classifying textual content from articles. Embrace a holistic learning experience that transcends the basics, providing a robust foundation for mastering the intricacies of NLP and Azure services.
Custom Text Classification & Extraction Feature: Dive into the intricacies of Azure AI Language services by harnessing the Custom text classification & extraction feature. This empowers the project to go beyond standard text processing, enabling nuanced analysis and extraction tailored to specific needs.
Containers and Blob Store Container: Integrate the power of containers seamlessly into the project, enhancing scalability and efficiency. Leveraging Azure Blob Storage containers, the project ensures secure and organized data storage, laying the foundation for robust text classification.
Single Label Classification: Immerse yourself in the intricacies of single-label classification, a pivotal concept in the NLP landscape. The project's focus on this aspect refines understanding and application, paving the way for precise categorization of textual data.
Data Labeling Techniques: Explore advanced data labeling methods to train the model effectively. The project guides learners through the intricate process of labeling data, a critical step in developing a robust model capable of accurate text classification.
Model Training and Evaluation: Delve into the complexities of model training, a pivotal phase in the NLP project lifecycle. Understand how to fine-tune the model for optimal performance and evaluate its effectiveness to ensure it aligns with predefined criteria.
Model Deployment Strategies: Learn diverse strategies for deploying models effectively. The project offers insights into the deployment phase, empowering individuals to make their models accessible through APIs, a crucial aspect of real-world applications.
Text Classification of Articles: The practical application extends to the classification of textual content from articles. Through the development of a small command-line application, test the model's efficacy in real-world scenarios, gaining hands-on experience in the actual application of the technology.
